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Microsoft Security Fundamentals

Microsoft has introduced a brand modern initiative with a view to survey more than three million Brazilian college students getting informed in topic matters round synthetic intelligence (AI).

all through the Microsoft AI+Tour adventure in São Paulo, the firm's chief govt Satya Nadella introduced the professional-bono partnership with school networks SESI and SENAI to tender AI working towards in extreme college lessons. during the announcement, Nadella cited that governments should quiet accelerate the adoption of automation however create modern capabilities on the identical time.

"we contain a massive chance to generate advances in digital technologies - and principally in AI - to empower each and every person and each corporation in Brazil to achieve greater," Nadella informed delegates on the experience on Tuesday (12).

four courses are available during the college community's online platform, protecting the introduction to AI and records science, information science fundamentals pile of solutions the usage of Azure, Bot and IoT. other lessons geared on the pile of functions and chatbots using Microsoft's cognitive features can be provided within the following pair of months, in addition to courses around cloud and innovation.

"teaching younger Brazilians with the applied sciences of the so-known as industry four.0 equivalent to AI is simple for Brazil to champion up with the technological revolution," spoke of Rafael Lucchesi, director benchmark at SENAI and director-superintendent at SESI.

"qualified authorities are the key for the country to be section of this global stream, for you to determine the generation of latest jobs and people's exceptional of lifestyles," he delivered.

in accordance with a examine performed with the aid of Brazilian think-tank Fundação Getúlio Vargas commissioned with the aid of Microsoft, which investigated the influence of AI on key markets in Brazil - agriculture, transport, communications, oil and gasoline and government - which jointly signify 36.four % of GDP and located that AI might boost GDP boom throughout these sectors by using up to 6,forty three percent over the subsequent 15 years.

A recent dissect found that automation technologies equivalent to AI may construct more than half of each and every formal employment vacancies inappropriate in Brazil. in keeping with the analysis, if Brazilian businesses continue to construct exhaust of automation technologies corresponding to synthetic intelligence on the present rate, it could influence 30 million jobs may be eliminated through 2026.

Delhi: Narasaraopeta Engineering college, Guntur, in partnership with Microsoft technology affiliate, will deportment a workshop on “AZURE Cloud”, Microsoft’s cloud computing platform, for engineering students privilege through 14-15 February 2019. The goal of the workshop, sponsored by Microsoft, is to train the members in the ideas and working at the back of Azure. The cloud carrier is ordinary for its superior security, privateness electricity, extreme availability, redundancy, and value-effectiveness. additionally, the workshop will include particulars about a number cloud capabilities enjoy computing, analytics, storage and networking. The primary kick of the workshop is to assist college students to stand out and score hired sooner.The state of Andhra Pradesh is in stern need of a talented team of workers to fullfil the requirements of the modern IT parks arising within the capital metropolis of Amravati making it faultfinding for working towards modern comers in accordance with technological advancement. leading Tech industry – Microsoft has been within the forefront in imparting technical schooling within the nation. Microsoft technology affiliate is a program that gives a role-primarily based certification to students, trying out them in quite a lot of technical roles and areas.speaking concerning the value of “IT” workshops, NEC Vice-chair- Mr M Chakravarthi mentioned, “students should be industry in a position. understanding the discipline be counted isn't adequate. we've been conducting industry visits to factories for electrical and mechanical students. This time they need to instruct students what each and every works and outfit are used within the IT trade”.

past, the college additionally had carried out workshops in “Introduction to Programming the exhaust of HTML & CSS” and “Introduction to Programming the usage of JavaScript” in affiliation with Microsoft expertise affiliate from January twenty eighth to 6th Feb 2019 to construct students time-honored with computer programming. extra, college students can be performing for the MTA exam for the topic of “Database Fundamentals”.

NEC has additionally been in industry partnership with Microsoft hosting “Microsoft Innovation Centre” on campus and other scholar evaluation courses. A observe-up certification examination was performed in a while.

No outcome discovered, try modern key phrase!in its place, they may quiet exhaust these fundamentals of security as guides in prioritization ... and Incident handling route. Microsoft windows security skilled Jason Fossen (@JasonFossen) will train his SEC505: ...

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Listings are topic to change. delight muster the venue in advance. To submit items to the calendar, fade to newssunonline.com/community.Monday

Income Tax Prep: for Low and moderate Income Taxpayers: From Feb. 2 through April 6, Moraine Township offers free assistance to low-and-moderate-income taxpayers filing their 2018 income tax returns through the IRS VITA Program (Volunteer Income Tax Assistance). Volunteers are trained and certified by the Internal Revenue Service (IRS) to prepare basic returns for taxpayers earning less than $55,000 in 2018. Appointments are available weekdays, evenings and weekends at the Moraine Township Office 800 Central Avenue Highland Park. The Township Office's regular industry hours, are from 8:30 a.m. to 4:30 p.m. Monday through Friday. Taxpayers are welcome to walk-in Saturdays from 9 a.m. to noon. muster today to schedule an appointment. 8:30 a.m. Monday, Moraine Township Office, 800 Central Ave., Highland Park, free, 847-780-6644.

Poetry Appreciation Club: If you are a fan of Dickinson, Poe, Alexie, Angelou, Frost, Whitman, or other poets, relate in gathering with fellow poetry lovers. They meet the third Monday of the month to read aloud and contend Great works of poetry. 7 p.m. Monday, Lake Forest Library, 360 E. Deerpath Rd., Lake Forest, free, 847-810-4610.

Digital Marketing – score Ready for Takeoff: Digital marketing is the marketing of tomorrow, and that means providing each customer and prospect with a tailored message. Recognize the need for a comprehensive marketing contrivance and implement it successfully. Become close with key marketing concepts. Learn how to engage an audience that will multiply sales and provide valuable metrics that you can analyze. noon Monday, College of Lake County - Southlake Campus, 1120 South Milwaukee Ave., Vernon Hills, $25, 847-543-2033.

Tuesday

Chicago's Black Metropolis: Highland Park Historical Society presents 'Chicago's Black Metropolis,' focusing on African-Americans whose energy and ingenuity helped Chicago become the commercial and cultural heart of the American Midwest. The program was initially presented to an audience in Paramaribo, Suriname in 2013 by Highland Park Historical Society Board Member Jeff Stern at the request of his daughter, who was then the Public Affairs Officer at the U.S. Embassy there. 7 p.m. Tuesday, Highland Park Public Library, 494 Laurel Ave., Highland Park, free, 847-432-7090.

Cyber Security Fundamentals: From computer viruses to phishing attacks to ransomware, cyber security threats are everywhere and are quickly becoming a more prevalent section of their everyday lives. This workshop will provide you with techniques for protecting you, your networks, devices and home. Topics include reasons why they are targets, common threats that exist "out in the wild" and protective measures. 11:30 a.m. Wednesday, College of Lake County, 19351 W. Washington St., Grayslake, $25, 847-543-2033.

Jim Cornelison with open Catalano Quartet at 210 Live : Jim Cornelison, National Anthem singer for the Chicago Blackhawks, joins forces with Grammy winning saxophonist open Catalano to perform music from their modern release "Steppin' Out." Hear them perform the music that Johnny Hartman, open Sinatra and many others made famous. 7:30 p.m. Wednesday, 210 Live, 210 Green Bay Rd., Highwood, $15 – $35, 847-433-0304.

Reading Rangers and Skype with the Author: Read this month's selected engage and relate in with activities and to partake your thoughts. Visit the Children's Library to check out your copy of this month's book, which is: The Sixty-Eight Rooms by Marianne Malone. Reading Rangers is typically for 8–12-year-olds in 2nd–5th grade, but tonight fans of each and every ages are welcome to attend, as the author, Marianne Malone joins via Skype. 7 p.m. Wednesday, Lake Forest Library, 360 E. Deerpath Rd., Lake Forest, free.

In this profit Net Security podcast, Chris Morales, Head of Security Analytics at Vectra, talks about machine learning fundamentals, and illustrates what cybersecurity professionals should know.

Here’s a transcript of the podcast for your convenience.

Hi, this is Chris Morales and I’m Head of Security Analytics at Vectra, and in this profit Net Security podcast I want to talk about machine learning fundamentals that I consider they each and every need to know as cybersecurity professionals.

AI has become very used within their industry more and more, and here at Vectra they are an AI company as well. As you start to hear more about AI, you contain to start asking yourself what is it really, what makes a machine intelligent and in the next ten minutes I just want to give a quick overview so that you can understand some of the principle operations and applications of how machine learnings apply to build AI, and just benign of a quick understanding of the different algorithms or understanding when you need to exhaust unavoidable algorithms for specific jobs.

There has always been a very muddled exhaust of the terms ersatz intelligence, data science and machine learning. For simple definitions, the term ersatz intelligence is applied when a machine mimics cognitive functions that humans associate with other human minds. These are functions such as learning and problem solving.

Where it gets confusing is that AI can exist without machine learning, and just a pair of decades ago, they used to talk quite often of something they muster an expert system. Data science doesn’t need to exhaust machine learning, machine learning is really just the exhaust of algorithms to attract signification out of that data and machine learning expert systems vary in the quantity of human erudition needed and how they are used. What I influence by that is that, in an expert system, the complete erudition of the expert is digitized and used in the conclusion making, and it ends up where the machine starts looking enjoy a string of if then statements.

Machine learning is each and every about having an algorithm learn from the data rather than having a human encode the logic such as that case and with machine learning people constantly talk about AI that their jobs are going to be lost. The reality of it is that there is plenty of work for humans to set up data, they contain to label data, they need to select features for these algorithms and then they contain to tune it to achieve the desired results. In other words, it’s not magic.

In machine learning, a model is a duty which learns to predict or classify by learning through input examples. They muster these examples data sets and each point in the data set is of the figure X and Y, where X is the input and Y is the output. What happens with machine learning is that the models fade through the entire set of data and it learns that data. If you provide a modern in point, thunder X here, the model can reveal you the Y. From X it understands Y and the model doesn’t execute this simply by memorizing the case data, but it actually learns the relationship between each X and Y within the data. What they influence is that it starts to understand the behaviors and what things peep enjoy and what they execute instead of what they are.

When they talk about machine learning, the two major types you will attend to survey are supervised and unsupervised, and then there is besides the concept of abysmal learning, and especially in security, these are the three that matter the most.

In supervised learning, if they talk about the X and the Ys, the data set has both X and Ys, which means that a data scientist already understood the inputs and the outputs and what the expected results were. He just wanted to train the data to learn from that so that it can find more of the same. In unsupervised learning there is no Ys provided for learning and the data that the data scientist work with has only Xs which is the inputs, so they don’t know the outcome. In those scenarios, unsupervised learning has to learn on modern things in true time in unavoidable environments.

Deep learning is a model of learning that can be applied to either supervised or unsupervised, it’s not specific to either, but it’s used to construct the machine smarter and they will fade into that in a minute. I consider the best thing here is to better understand the types of machine learning. I will try to draw an analogy with true life human learning examples. I’m not going to cover each and every topics for every ilk of algorithm but just some key high-level terms related to a machine learning that they consider of their interest and specific to cybersecurity.

The first kind, when they consider of supervised learning, going back to that, here is a real-world example. You’ve seen people smoke and they score sick or they score cancer. By that observation that you made as a human, you construct a conclusion that neither you nor your kids will ever smoke because you learnt in life that smoking kills. Mathematically, as a human you contain observed a ton of data, which is lots and lots of smokers and that they score cancer and they die, and you came up with the rule for classification. You decided that a unavoidable characteristic means class A else class B. That is a description, what they call, supervised learning and specifically classification where what you are trying to execute is predict a label.

The other ilk of supervised machine learning is regression learning, which is predicting the quantity. Fundamentally, classification is about predicting a label and regression is about predicting the quantity, but they are both supervised. An case of regression is, thunder you score the data for a house charge versus the region of where each and every these houses are, and then you sit down and you plot it on a graph and this is a pretty common thing people execute when they want to buy a house. When you peep at the data you survey that it’s almost a straight line and you draw a straight line. Now what happens is that when you peep at this graph you drew with each and every these houses placed on it, you can predict the charge of a house as someone tells you the region so that you start to understand the privilege areas you want to live in or where you want to buy a house because neighborhood A is a million dollar homes and neighborhood B is five hundred thousand dollar homes without knowing every unique home, and with the data you had you can construct predictions on future homes in those neighborhoods. This is exactly how true estate insurance and things enjoy that actually work, they predict these quantities. The dissimilarity between a human and a machine is a machine does it in a more mathematical and formal way, where humans exhaust natural intuition.

The next example, and those examples are for supervised learning, the next region of machine learning that they freight about in cybersecurity is unsupervised learning. When they talk about unsupervised machine learning they primarily focus on the concept of clustering. Clustering is creating groups of clusters based on similarities and examples. To give you another true world scenario for that and how they execute it as classic people, let’s thunder that an advertising platform (we each and every understand advertising – Google does it, Facebook, each and every the major companies) wants to segment the population into smaller groups for similar demographics and purchasing habits, and they execute this so that the advertisers can gain a specific target market with apposite ads.

Vectra as a cyber security company, they advertise with Google and they fade to Google and thunder “hey, they are looking for people who are interested in cybersecurity or looking for incident response” or things enjoy that. They gave a unavoidable criteria and then Google will assume those criteria and start to cluster and assume these groups of people and thunder “I don’t really know who these people are, but I know some habits about them, so I can construct a guess that these people are interested in security and that’s what they give us”. What happens here, from a machine perspective, is that mathematically you are faced with the chore of grouping unlabeled data, and rather than finding the group before looking at the data, clustering allows you to find and dissect the groups that formed organically.

Then the last area, and then I want to just benign of give a quick case of how this applies to security, is abysmal learning. The major thing they freight about here is a concept called transfer learning which means that I instruct the machine a chore and it reuses as a starting point for another model on a second task. In the true world an simple case that is: thunder your mom reveal you how to elect oranges, she sends you to the grocery store to buy apples that you don’t know how to, you can exhaust some common sense to construct this decision, or you know how to play tennis, it shouldn’t assume you long to pick up squash. This is essentially what an HR of a company terms as a transferable skill, and it’s this skill set that allows us to exhaust abysmal learning to start to learn about modern tasks that they never solved based on prior knowledge.

Just as a observation to tie this back to security, as they are coming to the top of their ten minutes here, is that each and every these examples of learning depict a fundamental shift in how they are able to perform security and, unlike a signature based approach that delivers a one for one mapping of threats to countermeasures, data science and machine learning uses the collective learning of each and every the threats observed in the past and the present and as things befall to proactively identify modern threats that haven’t been seen before.

Just for one more example, consider of it as a student learning a modern topic at school. Memorizing the answers to a test might result on a passing grade but that approach misses the brand when it comes to learning how to resolve a problem and that’s where they contain been in security with signatures. It’s a critically primary distinction when using data science to detect threats.

Long-term it’s essential to understand what, when, why and how and actual erudition in intelligence is far more advantageous when evaluating and solving modern problems that contain not been encountered before, and for the traditional signature model to continue working and secure how they contain been doing it, each and every the answers contain to be known ahead of time which is absolutely unrealistic. For case the domain atme.com has been seen behaving badly in the past, therefore it’s bad.

Data science expects to be asked true questions and it applies collective learning to evaluate an unknown. For the collective erudition of threats gathered from true world it’s feasible to identify the domain is acting detestable based on its deportment rather than maxim I saw it in the past. A scenario for that is atmeonetwothree.com has never been seen behaving badly in the past but traffic tuned from it showing different behaviors. Those combinations the machine learns or actually command in control with no prior knowledge.

To learn more, fade to vectra.ai and they contain a number of resources there. There is a twenty minute video from a presentation I gave at Infosecurity Europe about this topic and besides in their blog string one of their data principle scientist has been writing a string on understanding AI that explains the mechanics on how they execute it and how they exhaust it here, and if you want to gain out directly you can gain us at info@vectra.ai and with that thank you and contain a advantageous day.

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If you’re interested in secunet Security Networks Aktiengesellschaft (ETR:YSN), then you might want to consider its beta (a measure of partake charge volatility) in order to understand how the stock could impact your portfolio. Modern finance theory considers volatility to be a measure of risk, and there are two main types of charge volatility. First, they contain company specific volatility, which is the charge gyrations of an individual stock. Holding at least 8 stocks can reduce this benign of risk across a portfolio. The second ilk is the broader market volatility, which you cannot diversify away, since it arises from macroeconomic factors which directly affects each and every the stocks on the market.

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View their latest analysis for secunet Security Networks

What YSN’s beta value tells investors

Given that it has a beta of 0.89, they can surmise that the secunet Security Networks partake charge has not been strongly impacted by broader market volatility (over the last 5 years). If history is a advantageous guide, owning the stock should profit ensure that your portfolio is not overly sensitive to market volatility. Beta is worth considering, but it’s besides primary to consider whether secunet Security Networks is growing earnings and revenue. You can assume a peep for yourself, below.

XTRA:YSN Income Statement Export February 18th 19

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How does YSN’s size impact its beta?

secunet Security Networks is a diminutive cap stock with a market capitalisation of €688m. Most companies this size are actively traded. diminutive companies often contain a towering beta value, but they can be heavily influenced by company-specific events. This might account for why this stock has a low beta.

What this means for you:

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Future Outlook: What are well-informed industry analysts predicting for YSN’s future growth? assume a peep at their free research report of analyst consensus for YSN’s outlook.

Past Track Record: Has YSN been consistently performing well irrespective of the ups and downs in the market? fade into more detail in the past performance analysis and assume a peep at the free visual representations of YSN’s historicals for more clarity.

Other Interesting Stocks: It’s worth checking to survey how YSN measures up against other companies on valuation. You could start with this free list of prospective options.

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